docstr: Compute a 3-D max pool given 5-D input x.
  
  Parameters
  ----------
  self
      Input volume *[batch_size,d,h,w,d_in]*.
  kernel
      Convolution filters *[d,h,w]*.
  strides
      The stride of the sliding window for each dimension of input.
  padding
      SAME" or "VALID" indicating the algorithm, or list indicating
      the per-dimension paddings.
  data_format
      NDHWC" or "NCDHW". Defaults to "NDHWC".
  count_include_pad
      Whether to include padding in the averaging calculation.
  ceil_mode
      Whether to use ceil or floor for creating the output shape.
  divisor_override
      If specified, it will be used as divisor,
      otherwise kernel_size will be used.
  out
      optional output array, for writing the result to. It must have
      a shape that the inputs broadcast to.
  
  Returns
  -------
  ret
      The result of the pooling operation.
  
  Examples
  --------
  >>> x = ivy.arange(48.).reshape((2, 3, 2, 2, 2))
  >>> print(x.avg_pool3d(2, 2, 'VALID'))
  ivy.array([[[[[ 7.,  8.]]]],
         [[[[31., 32.]]]]])
  >>> print(x.avg_pool3d(2, 2, 'SAME'))
  ivy.array([[[[[ 7.,  8.]]],
          [[[19., 20.]]]],
         [[[[31., 32.]]],
          [[[43., 44.]]]]])
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code: def avg_pool3d(
      self: ivy.Array,
      kernel: Union[int, Tuple[int], Tuple[int, int, int]],
      strides: Union[int, Tuple[int], Tuple[int, int, int]],
      padding: str,
      /,
      *,
      data_format: str = "NDHWC",
      count_include_pad: bool = False,
      ceil_mode: bool = False,
      divisor_override: Optional[int] = None,
      out: Optional[ivy.Array] = None,
  ) -> ivy.Array:
      """Compute a 3-D max pool given 5-D input x.
  
      Parameters
      ----------
      self
          Input volume *[batch_size,d,h,w,d_in]*.
      kernel
          Convolution filters *[d,h,w]*.
      strides
          The stride of the sliding window for each dimension of input.
      padding
          SAME" or "VALID" indicating the algorithm, or list indicating
          the per-dimension paddings.
      data_format
          NDHWC" or "NCDHW". Defaults to "NDHWC".
      count_include_pad
          Whether to include padding in the averaging calculation.
      ceil_mode
          Whether to use ceil or floor for creating the output shape.
      divisor_override
          If specified, it will be used as divisor,
          otherwise kernel_size will be used.
      out
          optional output array, for writing the result to. It must have
          a shape that the inputs broadcast to.
  
      Returns
      -------
      ret
          The result of the pooling operation.
  
      Examples
      --------
      >>> x = ivy.arange(48.).reshape((2, 3, 2, 2, 2))
      >>> print(x.avg_pool3d(2, 2, 'VALID'))
      ivy.array([[[[[ 7.,  8.]]]],
             [[[[31., 32.]]]]])
      >>> print(x.avg_pool3d(2, 2, 'SAME'))
      ivy.array([[[[[ 7.,  8.]]],
              [[[19., 20.]]]],
             [[[[31., 32.]]],
              [[[43., 44.]]]]])
      """
      return ivy.avg_pool3d(
          self,
          kernel,
          strides,
          padding,
          data_format=data_format,
          count_include_pad=count_include_pad,
          ceil_mode=ceil_mode,
          divisor_override=divisor_override,
          out=out,
      )
  
imports: import abc
  from typing import Optional, Union, Tuple, List, Literal, Sequence, Callable
  import ivy
label_desc: Use image node_id 6 for calling the _ArrayWithLayersExperimental obj's underlying member method code with example usage: obj.avg_pool3d() and returns: ivy